辐射保真的红外遥感图像超分辨率重建

doi:10.19665/j.issn1001-2400.2019.02.018

Abstract

Abstract:

In order to overcome the contradiction between the super resolution reconstruction of an infrared remote sensing image and the fidelity of radiometric calibration, combined with the theory of regularized super-resolution reconstruction, a super resolution reconstruction model based on two-order total generalized variation is established. Through the analysis of the characteristics of the reconstruction model, the ADMM algorithm is introduced to solve the numerical solution. In the reconstruction process, the bilateral high-frequency filter is used to separate the high and low frequency information of the image, and after separation only the high frequency information image is dealt with. Finally, the low frequency information image and the reconstructed high frequency information image are fused to achieve super resolution. Experimental verification and quantitative analysis of the FY-4 meteorological satellite infrared image show that the influence of this method on radiometric calibration is less than that of conventional super resolution reconstruction.

Key words: radiation fidelity, super-resolution reconstruction, two order generalized variation, alternating directions method of multipliers, high frequency information image

CLC Number:

TP394.1

SHI Wenjun,GUO Congzhou,TONG Xiaochong,TIAN Yuan,CAO Wen. Super-resolution reconstruction of infrared remote sensing images with radiation fidelity[J].Journal of Xidian University, 2019, 46(2): 107-113.

Figures/Tables 9

References 14

[1]	顾行发, 田国良, 余涛 , 等. 航天光学遥感器辐射定标原理与方法[M]. 北京: 科学出版社, 2013: 1-68.
[2]	TOMASI C, MANDUCHI R . Bilateral Filtering for Gray and Color Images[C]// Proceedings of the 1998 IEEE International Conference on Computer Vision. Piscataway: IEEE, 1998: 839-846.
[3]	ZHENG Z, JIA S, ZHANG G , et al. An Approach of Point Cloud Denoising Based on Improved Bilateral Filtering[C]// Proceedings of the SPIE: 10615. Bellingham: SPIE, 2018: 1061557.
[4]	周峰, 金炜, 龚飞 , 等. 主题学习和稀疏表示的MODIS图像超分辨率重建[J]. 遥感学报, 2017,21(2):253-261.
	ZHOU Feng, JIN Wei, GONG Fei , et al, Super Resolution Reconstruction of MODIS Image Based on Topic Learning and Sparse Representation[J]. Journal of Remote Sensing, 2017,21(2):253-261.
[5]	BREDIES K, KUNISCH K, POCK T . Total Generalized Variation[J]. SIAM Journal on Imaging Sciences, 2010,3(3):492-526. doi: 10.1137/090769521
[6]	许建楼, 冯象初, 郝岩 . 二阶总广义变分图像修复模型及其算法[J]. 西安电子科技大学学报, 2012,39(5):18-23. doi: 10.3969/j.issn.1001-2400.2012.05.004
	XU Jianlou, FENG Xiangchu, HAO Yan . Second Order Total Generalized Variational Inpainting Model and Its Algorithm[J]. Journal of Xidian University, 2012,39(5):18-23. doi: 10.3969/j.issn.1001-2400.2012.05.004
[7]	KANG M, KANG M, JUNG M . Total Generalized Variation Based Denoising Models for Ultrasound Images[J]. Journal of Scientific Computing, 2017,72(1):172-197. doi: 10.1007/s10915-017-0357-3
[8]	伍政华, 孙明健, 顾宗山 , 等. 基于二阶广义方向性全变分的图像超分辨率重建方法[J]. 电子学报, 2017,45(11):2625-2632. doi: 10.3969/j.issn.0372-2112.2017.11.008
	WU ZHENGHUA, SUN MINGJIAN, GU ZONGSHAN , et al. Second-order Directional Total Generalized Variation Regularization for Image Super Resolution[J]. Acta Electronica Sinica, 2017,45(11):2625-2632. doi: 10.3969/j.issn.0372-2112.2017.11.008
[9]	BREDIES K, SUN H P . Preconditioned Douglas-Rachford Algorithms for TV- and TGV-regularized Variational Imaging Problems[J]. Journal of Mathematical Imaging and Vision, 2015,52(3):317-344. doi: 10.1007/s10851-015-0564-1
[10]	BREDIES K, VALKONEN T . Inverse Problems with Second-order Total Generalized Variation Constraints[EB/OL]. [ 2018- 04- 20]. http://tuomov.iki.fi/mathematics/SampTA2011.pdf.
[11]	刘孝艳, 冯象初 . 对称张量空间下高阶正则化的图像恢复模型[J]. 计算机科学, 2013,40(11A):359-362. doi: 10.3969/j.issn.1002-137X.2013.z2.090
	LIU Xiaoyan, FENG Xiangchu . Image Restoration Model of High Order Regularization in Symmetric Tensor Space[J]. Computer Science, 2013,40(11A):359-362. doi: 10.3969/j.issn.1002-137X.2013.z2.090
[12]	DENG W, YIN W . On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers[J]. Journal of Scientific Computing, 2016,66(3):889-916. doi: 10.1007/s10915-015-0048-x
[13]	WARREN R E, OSHER S J . Hyperspectral Unmixing by the Alternating Direction Method of Multipliers[J]. Inverse Problems and Imaging, 2017,9(3):917-933.
[14]	HE R, ZHENG W S, TAN T , et al. Half-quadratic Minimization for Robust Sparse Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,36(2) : 261-275. doi: 10.1109/TPAMI.2013.102

数据名称	波段1	波段2	截取区域坐标
原始数据	1.64	1.57	(1:200,1:200)
文中超分辨率重建	1.64	1.57	(1:400,1:400)
常规超分辨率重建	1.74	1.69	(1:400,1:400)

绝对误差	波段1		波段2
绝对误差	常规超分辨率	文中方法超分辨率	常规超分辨率	文中方法超分辨率
<1%	21.06	31.60	40.35	48.27
<2%	40.27	46.79	61.55	65.96
<5%	65.30	70.29	82.23	87.96
<10%	76.54	87.42	91.19	97.24

绝对误差	波段1		波段2
绝对误差	k	b	k	b
常规超分 (置信区间)	0.93690 (0.93480, 0.93810)	0.01702 (0.01593, 0.01920)	0.95890 (0.95880, 0.95910)	0.01401 (0.01393, 0.01410)
文中超分 (置信区间)	0.989800 (0.989700, 0.989900)	0.002072 (0.002026, 0.002128)	0.984800 (0.984700, 0.985000)	0.001785 (0.001766, 0.001803)

Super-resolution reconstruction of infrared remote sensing images with radiation fidelity

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References 14

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